




'''simple docstring'''


import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


UpperCAmelCase_					=   datasets.utils.logging.get_logger(__name__)





@dataclass
class  __lowercase    (	datasets.BuilderConfig			):
   _a    						=   None
   _a    						=   "utf-8"
   _a    						=   None
   _a    						=   None
   _a    						=   True  # deprecated
   _a    						=   None  # deprecated
   _a    						=   10 << 20  # 10MB
   _a    						=   None





class  __lowercase    (	datasets.ArrowBasedBuilder			):
   _a    						=   JsonConfig







   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     if self.config.block_size is not None:
       logger.warning('The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'   )
       __a								=       self.config.block_size
     if self.config.use_threads is not True:
       logger.warning(
           'The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.'   )
     if self.config.newlines_in_values is not None:
       raise ValueError('The JSON loader parameter `newlines_in_values` is no longer supported'   )
     return datasets.DatasetInfo(features=self.config.features   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Dict:
     if not self.config.data_files:
       raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}"   )
     __a								=       dl_manager.download_and_extract(self.config.data_files   )
     if isinstance(UpperCamelCase   ,				(str, list, tuple)   ):
       __a								=       data_files
       if isinstance(UpperCamelCase   ,				UpperCamelCase   ):
         __a								=       [files]
       __a								=       [dl_manager.iter_files(UpperCamelCase   ) for file in files]
       return [datasets.SplitGenerator(name=datasets.Split.TRAIN   ,				gen_kwargs={'files': files}   )]
     __a								=       []
     for split_name, files in data_files.items():
       if isinstance(UpperCamelCase   ,				UpperCamelCase   ):
         __a								=       [files]
       __a								=       [dl_manager.iter_files(UpperCamelCase   ) for file in files]
       splits.append(datasets.SplitGenerator(name=UpperCamelCase   ,				gen_kwargs={'files': files}   )   )
     return splits







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     pa.Table:
     if self.config.features is not None:
       # adding missing columns
       for column_name in set(self.config.features   ) - set(pa_table.column_names   ):
         __a								=       self.config.features.arrow_schema.field(UpperCamelCase   ).type
         __a								=       pa_table.append_column(UpperCamelCase   ,				pa.array([None] * len(UpperCamelCase   )   ,				type=UpperCamelCase   )   )
       # more expensive cast to support nested structures with keys in a different order
       # allows str <-> int/float or str to Audio for example
       __a								=       table_cast(UpperCamelCase   ,				self.config.features.arrow_schema   )
     return pa_table







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Dict:
     for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCamelCase   )   ):
       # If the file is one json object and if we need to look at the list of items in one specific field
       if self.config.field is not None:
         with open(UpperCamelCase   ,				encoding=self.config.encoding   ,				errors=self.config.encoding_errors   ) as f:
           __a								=       json.load(UpperCamelCase   )

         # We keep only the field we are interested in
         __a								=       dataset[self.config.field]

         # We accept two format: a list of dicts or a dict of lists
         if isinstance(UpperCamelCase   ,				(list, tuple)   ):
           __a								=       set().union(*[row.keys() for row in dataset]   )
           __a								=       {col: [row.get(UpperCamelCase   ) for row in dataset] for col in keys}
         else:
           __a								=       dataset
         __a								=       pa.Table.from_pydict(UpperCamelCase   )
         yield file_idx, self._cast_table(UpperCamelCase   )

       # If the file has one json object per line
       else:
         with open(UpperCamelCase   ,				'rb'   ) as f:
           __a								=       0
           # Use block_size equal to the chunk size divided by 32 to leverage multithreading
           # Set a default minimum value of 16kB if the chunk size is really small
           __a								=       max(self.config.chunksize // 32   ,				16 << 10   )
           __a								=       (
               self.config.encoding_errors if self.config.encoding_errors is not None else 'strict'
           )
           while True:
             __a								=       f.read(self.config.chunksize   )
             if not batch:
               break
             # Finish current line
             try:
               batch += f.readline()
             except (AttributeError, io.UnsupportedOperation):
               batch += readline(UpperCamelCase   )
             # PyArrow only accepts utf-8 encoded bytes
             if self.config.encoding != "utf-8":
               __a								=       batch.decode(self.config.encoding   ,				errors=UpperCamelCase   ).encode('utf-8'   )
             try:
               while True:
                 try:
                   __a								=       paj.read_json(
                       io.BytesIO(UpperCamelCase   )   ,				read_options=paj.ReadOptions(block_size=UpperCamelCase   )   )
                   break
                 except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                   if (
                       isinstance(UpperCamelCase   ,				pa.ArrowInvalid   )
                       and "straddling" not in str(UpperCamelCase   )
                       or block_size > len(UpperCamelCase   )
                   ):
                     raise
                   else:
                     # Increase the block size in case it was too small.
                     # The block size will be reset for the next file.
                     logger.debug(
                         f"Batch of {len(UpperCamelCase   )} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}."   )
                     block_size *= 2
             except pa.ArrowInvalid as e:
               try:
                 with open(
                     UpperCamelCase   ,				encoding=self.config.encoding   ,				errors=self.config.encoding_errors   ) as f:
                   __a								=       json.load(UpperCamelCase   )
               except json.JSONDecodeError:
                 logger.error(f"Failed to read file '{file}' with error {type(UpperCamelCase   )}: {e}"   )
                 raise e
               # If possible, parse the file as a list of json objects and exit the loop
               if isinstance(UpperCamelCase   ,				UpperCamelCase   ):  # list is the only sequence type supported in JSON
                 try:
                   __a								=       set().union(*[row.keys() for row in dataset]   )
                   __a								=       {col: [row.get(UpperCamelCase   ) for row in dataset] for col in keys}
                   __a								=       pa.Table.from_pydict(UpperCamelCase   )
                 except (pa.ArrowInvalid, AttributeError) as e:
                   logger.error(f"Failed to read file '{file}' with error {type(UpperCamelCase   )}: {e}"   )
                   raise ValueError(f"Not able to read records in the JSON file at {file}."   ) from None
                 yield file_idx, self._cast_table(UpperCamelCase   )
                 break
               else:
                 logger.error(f"Failed to read file '{file}' with error {type(UpperCamelCase   )}: {e}"   )
                 raise ValueError(
                     f"Not able to read records in the JSON file at {file}. "
                     f"You should probably indicate the field of the JSON file containing your records. "
                     f"This JSON file contain the following fields: {str(list(dataset.keys()   )   )}. "
                     f"Select the correct one and provide it as `field='XXX'` to the dataset loading method. "   ) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
             yield (file_idx, batch_idx), self._cast_table(UpperCamelCase   )
             batch_idx += 1









'''simple docstring'''


import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment





@require_huggingface_suite
class  __lowercase    (	unittest.TestCase			):







   def 		UpperCamelCase__      (						self   )					->     Tuple:
     __a								=       inspect.getfile(accelerate.test_utils   )
     __a								=       os.path.sep.join(
         mod_file.split(os.path.sep   )[:-1] + ['scripts', 'external_deps', 'test_metrics.py']   )

     from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

     __a								=       test_metrics







   @require_cpu
   def 		UpperCamelCase__      (						self   )					->     Tuple:
     debug_launcher(self.test_metrics.main   ,				num_processes=1   )







   @require_cpu
   def 		UpperCamelCase__      (						self   )					->     str:
     debug_launcher(self.test_metrics.main   )







   @require_single_gpu
   def 		UpperCamelCase__      (						self   )					->     Dict:
     self.test_metrics.main()







   @require_multi_gpu
   def 		UpperCamelCase__      (						self   )					->     int:
     print(f"Found {torch.cuda.device_count()} devices."   )
     __a								=       ['torchrun', f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
     with patch_environment(omp_num_threads=1   ):
       execute_subprocess_async(UpperCamelCase   ,				env=os.environ.copy()   )



